Separating Movement and Gravity Components in an Acceleration Signal and Implications for the Assessment of Human Daily Physical Activity

By V. V. van Hees, Lukas Gorzelniak, Emmanuel Carlos Dean León, M. Eder, Marcelo R. Pias, S. Taherian, Ulf Ekelund, F. Renström, P. Franks, A. Horsch and 1 more. PLoS ONE, 2013

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Introduction Human body acceleration is often used as an indicator of daily physical activity in epidemiological research. Raw acceleration signals contain three basic components: movement, gravity, and noise. Separation of these becomes increasingly difficult during rotational movements. We aimed to evaluate five different methods (metrics) of processing acceleration signals on their ability to remove the gravitational component of acceleration during standardised mechanical movements and the implications for human daily physical activity assessment. Methods An industrial robot rotated accelerometers in the vertical plane. Radius, frequency, and angular range of motion were systematically varied. Three metrics (Euclidian norm minus one [ENMO], Euclidian norm of the high-pass filtered signals [HFEN], and HFEN plus Euclidean norm of low-pass filtered signals minus 1 g [HFEN+]) were derived for each experimental condition and compared against the reference acceleration (forward kinematics) of the robot arm. We then compared metrics derived from human acceleration signals from the wrist and hip in 97 adults (22–65 yr), and wrist in 63 women (20–35 yr) in whom daily activity-related energy expenditure (PAEE) was available. Results In the robot experiment, HFEN+ had lowest error during (vertical plane) rotations at an oscillating frequency higher than the filter cut-off frequency while for lower frequencies ENMO performed better. In the human experiments, metrics HFEN and ENMO on hip were most discrepant (within- and between-individual explained variance of 0.90 and 0.46, respectively). ENMO, HFEN and HFEN+ explained 34%, 30% and 36% of the variance in daily PAEE, respectively, compared to 26% for a metric which did not attempt to remove the gravitational component (metric EN). Conclusion In conclusion, none of the metrics as evaluated systematically outperformed all other metrics across a wide range of standardised kinematic conditions. However, choice of metric explains different degrees of variance in daily human physical activity.

Subfields: Computer Vision and Pattern Recognition, Physical Therapy, Sports Therapy and Rehabilitation, Physiology

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Citations in the corpus

GGIR: A Research Community–Driven Open Source R Package for Generating Physical Activity and Sleep Outcomes From Multi-Day Raw Accelerometer Data (2019, methodology): “The data recorded are typically expressed in gravitational acceleration ( g ) because this is the reference point for acceleration value calibration, re ﬂ ecting both the movement and gravitational component (van Hees et al., 2013).”
Age group comparability of raw accelerometer output from wrist- and hip-worn monitors. (2014, methodology): “For this, the vector magnitude (VM) was taken from the three axes and then subtracted by the value of gravity (g) as in (x(2) + y(2) + z(2))(2) – 1, after which, negative values were rounded up to zero, referred to as Euclidian norm minus one (ENMO) in a previous study (37).”
Autocalibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: an evaluation on four continents (2014, methodology): “Metric ENMO may be more appropriate for energy expenditure estimation and easier for researchers to describe, replicate, and interpret (11).”